Dual adaptive training of photonic neural networks
- URL: http://arxiv.org/abs/2212.06141v1
- Date: Fri, 9 Dec 2022 05:03:45 GMT
- Title: Dual adaptive training of photonic neural networks
- Authors: Ziyang Zheng, Zhengyang Duan, Hang Chen, Rui Yang, Sheng Gao, Haiou
Zhang, Hongkai Xiong, Xing Lin
- Abstract summary: Photonic neural network (PNN) computes with photons instead of electrons to feature low latency, high energy efficiency, and high parallelism.
Existing training approaches cannot address the extensive accumulation of systematic errors in large-scale PNNs.
We propose dual adaptive training ( DAT) that allows the PNN model to adapt to substantial systematic errors.
- Score: 30.86507809437016
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Photonic neural network (PNN) is a remarkable analog artificial intelligence
(AI) accelerator that computes with photons instead of electrons to feature low
latency, high energy efficiency, and high parallelism. However, the existing
training approaches cannot address the extensive accumulation of systematic
errors in large-scale PNNs, resulting in a significant decrease in model
performance in physical systems. Here, we propose dual adaptive training (DAT)
that allows the PNN model to adapt to substantial systematic errors and
preserves its performance during the deployment. By introducing the systematic
error prediction networks with task-similarity joint optimization, DAT achieves
the high similarity mapping between the PNN numerical models and physical
systems and high-accurate gradient calculations during the dual backpropagation
training. We validated the effectiveness of DAT by using diffractive PNNs and
interference-based PNNs on image classification tasks. DAT successfully trained
large-scale PNNs under major systematic errors and preserved the model
classification accuracies comparable to error-free systems. The results further
demonstrated its superior performance over the state-of-the-art in situ
training approaches. DAT provides critical support for constructing large-scale
PNNs to achieve advanced architectures and can be generalized to other types of
AI systems with analog computing errors.
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